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Behavioral Economics and Health

In preparation for the Oxford Textbook of Public Health

By Judd B. Kessler and C. Yiwei Zhang

Abstract

Behavioral Economics combines the insights of Economics and Psychology to

identify how individuals deviate from the standard assumptions of economic theory and

to build systematic deviations into improved models of human behavior. These models

allow researchers to better describe and predict individual behavior. Lessons from

Behavioral Economics can be leveraged to design large-scale public health interventions

and achieve policy goals.

This chapter begins with a broad overview of Behavioral Economics and

identifies settings in which policy makers may wish to intervene in health decisions. The

rest of the chapter explores four major topic areas within Behavioral Economics —

reward incentives, information and salience, context and framing, and social forces —

and investigates their influence on health behaviors including medication adherence,

obesity and weight control, and medical donation.

Within each of the four topic areas we discuss the relevant predictions of standard

economic theory, we provide evidence of the behavioral forces that lead individuals to

deviate from these predictions, and then we describe various public health interventions

that have leveraged the lessons of Behavioral Economics to achieve policy goals.

Keywords: Behavioral Economics, Economics, externalities, interventions, commitment

contracts, loss aversion, bounded rationality, default effects, social forces, salience,

framing

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I. Introduction

Behavioral Economics is a field at the intersection of Economics and Psychology.

Standard economic theory is built on the assumption that individuals are fully rational,

completely selfish, forward-thinking decision makers. This set of assumptions has

allowed economists to predict behavior using simple and tractable analytical models. But

research from both Economics and Psychology has demonstrated that individuals

regularly deviate from the predictions of standard economic theory and do so in

systematic ways. Behavioral Economics aims to (1) explain why individuals deviate from

the assumptions of standard economic theory and (2) use these insights to advance our

models of individual behavior.

By improving our models, Behavioral Economics allows policy makers to design

interventions — like the health interventions that are described in this chapter — to more

effectively achieve policy goals. In this way, Behavioral Economics is both descriptive,

giving us a better picture of what behavior looks like (and why it looks that way), and

prescriptive, suggesting how policy can most effectively impact individual decision-

making.

In this chapter, we discuss four major topic areas within Economics and

Behavioral Economics: (1) reward incentives, (2) information and salience, (3) context

and framing, and (4) social forces. We will address each topic area with a section of the

chapter. Within each section, we will highlight some of the topic area’s most influential

papers — including some from outside the health domain, which we believe provide

relevant background.

Throughout the chapter, we describe research on a number of important health

behaviors, including: medication adherence, obesity and weight control, and medical

donation. While we touch on a number of other health behaviors in the chapter, we

describe these three (and the reasons we find them of particular interest) in the following

section.

It is worth noting that this chapter is by no means exhaustive in its coverage of the

Behavioral Economics insights that might influence health behaviors or of the health

behaviors that might be subject to the insights we discuss. Instead, we have picked a few

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illustrative settings where we have seen fruitful application of Behavioral Economics

research and expect to see more in the coming years.

Externalities

Since many papers described in this chapter analyze interventions that affect

health behaviors, it is worth outlining in broad strokes what standard economic theory

says about when policy makers should be intervening in the health domain. This exercise

provides a baseline for thinking through the additional policy interventions that might be

justified by results from Behavioral Economics.

As noted above, standard economic theory models individuals as fully rational,

selfish, and forward thinking. Individuals who satisfy these assumptions (who we simply

call “rational” throughout) are usually better off when left to their own devices. If a

rational individual fails to take his doctor-prescribed medication, his decision is likely

best for him, since by assumption he has thought through all the costs and benefits and

decided that the costs (e.g. the cost of purchasing his medication and the pain of the side

effects) outweigh the potential benefits (e.g. his lower risk of a heart attack).

Under the assumption that individuals are rational, there are generally only two

reasons for intervention. First, if the rational individual suffers from a constraint that

prevents him from implementing his preferred choice (e.g. he cannot afford to purchase

his medication even though he would like to take it) then a policy maker may want to

help the individual by loosening those constraints (e.g. providing him with a loan to help

pay for his medication). Second, a policy maker may want to intervene if a

decision creates externalities. Externalities are indirect effects on other agents that an

individual does not fully consider when making his decisions. For example, if the patient

in the example above cannot afford his cost of care for his heart attack, then when he fails

to take his medication he forces society (i.e. the government and thus all tax payers) to

cover some of his higher costs. Consequently, his decision about whether or not to take

his medication affects people beside himself. That individuals fail to consider the costs

imposed on other agents (or the benefits incurred by other agents) generally leads to

socially inefficient outcomes.

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Externalities are the key rationale for policy interventions across a number of

domains both inside and outside of health. The government taxes pollution created by

companies and individuals since pollution imposes negative externalities on those

exposed to it. Regulation that forbids individuals from blasting music at 3 AM is

designed to discourage the negative externality a rowdy party might have on neighbors

who want to go to sleep.

As hinted above, a number of activities in the health domain create externalities.

Some of the externalities are obvious while others are not. On the obvious end of the

spectrum, medical donations (e.g. donations of blood, bone marrow, tissues, and organs)

create positive externalities since they clearly benefit other people. Individuals who

receive donor kidneys can expect better health outcomes than if they remained on

dialysis. Some forms of preventative care are both beneficial to the recipient and create

positive externalities for the health of other people. Receiving a flu-shot or a vaccine

helps keep the recipient from getting sick but also plays an important role in preventing

the spread of disease since inoculated individuals are less likely to spread a virus. At the

less obvious end of the spectrum, many activities that make a person healthier (e.g.

healthy eating, exercise, medication adherence, and smoking cessation) can generate

positive externalities for society since most individuals do not pay the full cost of their

medical care and are instead covered in part by health insurance, the government, or a

combination of the two. Consequently, requiring less care or staying healthy for longer

(and requiring care later in life) makes the individual better off and lessens the cost paid

by others in insurance premiums and taxes.

Given that many health activities have positive externalities, it may be socially

beneficial to encourage individuals to engage in them. Economics and Behavioral

Economics can provide strategies to most effectively encourage people to eat healthy,

take their medication, donate their organs, get flu shots, and so on.

Behavioral Biases

When rational individuals do not face constraints that restrict their choice and do

not impose externalities with their actions, then there is little scope for intervention, even

if we disagree with individuals’ choices. An individual who can afford his medication

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and pays for all his health costs out of pocket may choose not to take his medication

because he simply prefers not to. If he is fully informed about the costs and benefits and

he is rational, we can do no better than to trust his judgment.

Behavioral Economics has demonstrated, however, that individuals are not fully

rational in the way standard economic theory predicts. Individuals who are not fully

rational (who we will call “behavioral”) might suffer from behavioral biases that make it

difficult for them to achieve the behavior they actually prefer. This introduces another

rationale for intervention: helping individuals achieve their own desired behavior.

For example, one way in which behavioral individuals may make sub-optimal

choices is by displaying present bias. In particular, individuals often overweight costs

and benefits incurred today (i.e. the present) relative to the costs and benefits incurred

tomorrow (i.e. the future). This type of bias can lead individuals to forgo healthy

behaviors and do so in a way that is inconsistent across time. Imagine an individual

deciding whether to go to the gym, which has a cost today in terms of time and fatigue

but has health benefits in the future. When considering his choice today, he may

overweight the time and fatigue costs — since they are incurred in the present — and so

decide to skip the gym. When considering his choice for tomorrow, however, he would

not overweight those costs. When considering tomorrow, he may prefer to go to the gym

and even believe that he will do so. When tomorrow arrives, however, it has become the

present, and he again overweighs the time and fatigue costs and again skips the workout.

This inconsistent behavior is not unique to going to the gym. It can explain why

individuals procrastinate about eating health, quitting smoking, or getting a flu shot. Each

of these activities features a present cost (e.g. sacrificing something you enjoy, giving up

time, incurring physical discomfort) and a delayed benefit (e.g. better health) and so

individuals might perpetually wait to incur those costs until a never-arriving tomorrow. If

our patient from the medication example earlier in this section were present biased, he

might skip taking his pill because he overweighs the small costs he faces today like

paying for the prescription and experiencing side effects. We consider these examples

mistakes because if the behavioral agent could commit today to force himself to go to the

gym (or eat the apple, or quit smoking, or get the flu shot, or take his pill) tomorrow, he

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would choose to do so. Behavioral individuals may want help making choices and

following through with them.

Present bias is just one of many behavioral phenomena that might make

individuals deviate from their own desired behavior. For example, a behavioral individual

might forget to take his medication, which a rational agent would not do. A behavioral

individual might not attend to all the relevant data needed to make an informed decision

about whether to take the pill or might fail to aggregate the data he does consider.

Behavioral Economics can help identify cases where individuals might have

trouble (e.g. due to present bias or forgetfulness) and suggest strategies to help

individuals achieve their ideal behaviors (e.g. commitment contracts, discussed in Section

III; or reminders, discussed in Section IV). As we go through each topic area, we will

describe the way individual behavior differs from the predictions of standard economic

theory. We will then describe interventions — suggested by Economics and Behavioral

Economics — that can encourage individuals to improve their own decision-making and

generate better health outcomes for the decision maker and better outcomes for society at

large.

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II. Health Behaviors

Here we highlight three health behaviors that we reference repeatedly throughout

the chapter. We think these behaviors are of particular interest for interventions

motivated by Behavioral Economics.

Medication Adherence

The failure of individuals to properly adhere to medication regimens prescribed

by their doctors is a major issue in healthcare. Non-adherence can often lead to serious

health consequences as well as increased health care costs down the road. Estimates from

Osterberg and Blaschke (2005) suggest that around half of all medication-related hospital

admissions in the U.S. are a result of failure to adhere properly to medication. These

hospital admissions are estimated to cost approximately $100 billion a year. While

medication expense may be part of the explanation, non-adherence is a problem even

among patients who face zero copays and so can get their medication for free (see Doshi

et al. 2009). Cutler and Everett (2010) suggest various reasons why people fail to adhere

to medication including: lifestyle, psychological issues, health literacy, support systems,

and side effects. Behavioral Economics interventions that help individuals overcome

some of the common barriers to adherence can generate better health outcomes and lower

cost of care.

Obesity and Weight Control

Obesity is a major problem in health care and is blamed for over 110,000 deaths a

year in the United States alone (Flegal et al. 2007). Flegal et al. (2010) calculate that 68

percent of Americans are overweight or obese. Schroeder (2007) suggests that decreasing

obesity — along with decreasing smoking — can lead to substantial improvements in the

state of health in the United States. Individuals often have trouble controlling their

weight. DellaVigna and Malmendier (2006) find that individuals buy expensive monthly

gym memberships but go so infrequently that paying per visit would cost less. This result

suggests that individuals have trouble making it to the gym even though they want to and

intend to go. Behavioral Economics can help explain the difficulty individuals have in

sticking to a weight-loss regimen and can provide strategies or interventions to help

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people succeed.

Medical Donation

There are a number of forms of medical donation, including blood and plasma

donation, bone marrow donation, and organ and tissue donation. Medical donation is an

important area of health research since these donations can have a substantial impact on

health outcomes (Schnitzler et al. 2005a,b) and cost of care (Dew 1997) and there is often

significant need. As of March 2013 over 117,000 individuals in the U.S. were on the

waiting list for an organ transplant (UNOS 2013). Registered organ donors make their

organs available for transplant upon their death, and one deceased donor can provide up

to eight life-saving organs. However, only 43% of Americans over the age of 18 were

registered as organ donors (Donate Life America 2012). Behavioral Economics

interventions may be particularly relevant in motivating medical donation since monetary

incentives are often not allowed in medical donation (Roth 2007).

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III. Reward Incentives

Reward incentives are a natural place to start an investigation of how economic

and behavioral economic forces impact health behaviors. Reward incentives show how

traditional economic interventions work and how these interventions can be improved by

insights from Behavioral Economics.

In standard economic theory, individuals value money and other tangible rewards

and engage in effort to get them. People go to work to earn money and we expect them to

work harder when there is more money at stake. Consequently, standard economic theory

suggests that reward incentives — particularly monetary incentives — can motivate

individuals to engage in behaviors that they otherwise would avoid. We begin this section

by describing interventions that utilize standard reward incentives in the health domain.

Behavioral Economics has made two important advances with regard to reward

incentives and how they affect behavior. First, it has suggested that not all incentives are

created equal. Individuals overweight small probability events and feel losses more

severely than equivalent gains. A reward incentive that leverages these biases might be

more effective for the same expected value. Second, and much more troubling for

standard economic theory, Behavioral Economics has shown that in certain domains

individuals respond in perverse ways to reward incentives. In some settings, monetary

incentives lead individuals to respond with less effort rather than more.

After describing settings where standard reward incentives motivate health

behaviors, we catalogue examples of interventions that leverage behavioral biases in

designing reward incentives. Finally, we describe settings where reward incentives

backfire.

Standard Reward Incentives

There are a number of health domains where standard reward incentives have

been shown to successfully influence health behaviors. Here we highlight their use in

three domains: obesity and weight control, smoking cessation, and medical donation.

In the context of obesity and weight control, researchers have used monetary and

non-monetary incentives to encourage more frequent gym attendance. Charness and

Gneezy (2009) report the results of two such studies on incentives and gym attendance.

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In the first study, they compare the gym attendance of three groups of college students

participating in a randomized experiment. One group was provided only with information

regarding the value of exercise. A second group was provided with the same information

and received an additional $25 if they visited the gym once in the following week. A

third group was treated like the second group but received an additional $100 if they

visited the gym 8 more times in the following four weeks. The authors find that

individuals respond to monetary incentives. Those who were paid to go to the gym were

much more likely to do so, and those who were paid for more visits went more often. The

second study took a similar form but included biometric measures to better gauge health

improvements. The study found that individuals paid to go to the gym more often had

lower body fat, lower BMI, and improved on a number of other health measures. The

authors provide evidence that the effects from both studies persist; those who were paid

to go to the gym continued to do so at higher rates for a few months after the incentives

were removed. This result suggests that these individuals may have formed a habit of

gym attendance. As will be seen throughout this section, this potential habit formation is

quite rare in the context of reward incentives.

A similar study by Acland and Levy (2011) also finds that individuals were more

likely to go to the gym when paid to do so. They also observe that the effect persists in

the weeks after the incentive was removed. After a semester break, however, the group

that had seemed to form a habit for gym attendance was no longer more likely to attend,

suggesting that the habit was short-lived.

In the context of smoking cessation, Volpp et al. (2009) report the results from an

experiment at a multinational company. Half of the employees in their sample were given

information about smoking cessation programs while the other half received the same

information plus incentives worth a total of $750 for enrolling in a program, quitting

smoking within 6 months, and staying quit for an additional 6 months. Individuals with

the incentives were significantly more likely to complete the program, quit, and stay quit.

In the context of medical donation, the use of monetary incentives is often

restricted. For example, the National Organ Transplant Act of 1984 prohibits the use of

“valuable consideration” to induce organ donation. Fortunately, standard economic

theory does not require that reward incentives be monetary for them to be effective. In the

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context of medical donation, a variety of non-monetary reward incentives have been

shown to generate increased willingness to donate.

Individuals are more likely to donate blood when provided with coupons for

merchandise (Ferrari et al. 1985), lottery tickets (Goette and Stutzer 2008), and other

incentives (for a summary of blood donation incentives, see Goette, Stutzer and Frey

2010). Lacetera, Macis and Slonim (2012) do an extensive empirical analysis of

participation in American Red Cross blood drives and find that drives with non-monetary

incentives for blood donation (including blankets, T-shirts, mugs, and coupons to

retailers) generate more donors. In addition, the larger the economic value of the

incentive, the bigger the increase in donors. They caution, however, that some of the

increase in donation may be the result of substitution away from nearby drives that do not

have incentives. A review of a variety of blood donation research supports the claim that

incentives increase blood donation without affecting quality (Lacetera, Macis and Slonim

2013). Related work has demonstrated that individuals are more likely to become bone

marrow donors when legislation that provides donors with paid leave and tax incentives

is in place (Lacetera, Macis and Stith 2012).

A particular non-monetary incentive that might motivate individuals to register as

organ donors is priority on organ donation waiting lists for those who register but end up

needing organs rather than being in a position to provide them. This non-monetary

incentive — a higher likelihood of receiving a transplantable organ or receiving a

transplantable organ more quickly — can be given to registered donors simply by

changing the way organs are allocated. Both Israel and Singapore currently provide

priority on organ donor waiting lists to registered donors. Kessler and Roth (2012)

investigate the effect of a priority rule on a laboratory game designed to look like organ

donor registration and show that it substantially increase likelihood of donation.

Early data from Israel, which implemented the policy fully in 2012, suggests that

the policy may have increased the number of deceased organ donors and the organ

donation rate (Lavee et al. 2013). One quirk of the Israeli policy, however, is that it has

the potential for a loophole that allows individuals to receive priority without ever being

in a position to donate their organs. A follow-up study (Kessler and Roth 2013)

investigates the effect of a loophole and finds that in a laboratory setting, such a loophole

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can eliminate the beneficial effect of the priority rule. Allowing individuals to receive the

priority without paying the costs of donation completely eliminates the effectiveness of

the priority rule. In addition, when subjects receive feedback about the use of the

loophole they become less likely to register as donors than when no priority system is

available, suggesting that how these priority systems are implemented can be crucial to

their success.

Designing Incentives using Behavioral Economics

As noted above, Behavioral Economics has shown that individuals overweight

small probability events and feel losses more severely than equivalent gains (called loss

aversion, see Kahneman and Tversky 1979). In addition, individuals do not like to feel

regret (called regret aversion).

Lotteries can take advantage of the fact that individuals overweight small

probability events. For example, providing a 1% chance of winning $100 might motivate

people more substantially than offering them $1 directly, even though the two have the

same expected value.

Combining probability weighting with loss aversion and regret aversion, some

studies have motivated individuals to take health actions using regret lotteries. In a regret

lottery, all individuals are entered into the lottery and informed about whether their name

is picked. If an individual fails to take a required action, however, she fails to earn the

lottery prize when picked. Even if a 1% chance of winning $100 is not enough to

motivate an individual to take an action (e.g. going to the gym today), she might go to the

gym to avoid the potential distress of knowing she would have won $100 but lost it by

skipping her workout. The regret lottery leverages overweighting of small probabilities

through the lottery, loss aversion by framing the earnings as money that is lost, and regret

aversion since it threatens individuals who fail to take the rewarded action with feelings

of regret.

Regret lotteries have successfully influenced behavior in a variety of health

domains. In the context of medication adherence, Volpp et al. (2008) find that providing

a regret lottery worth either $3 or $5 in expected value significantly increases the

likelihood that individuals correctly take their prescribed medications. In the context of

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weight loss, Volpp et al. (2008) find that subjects lose significantly more weight when

provided with a regret lottery that pays out an expected $3 per day (in the form of a 20%

chance of winning $10 plus a 1% chance of winning $100) if the subject is on track to his

or her weight loss goal.

Another way to leverage loss aversion with monetary incentives is to provide

individuals the opportunity to make commitment contracts (also called deposit contracts)

in which they put up their own money. This money is then forfeited if they fail to achieve

a certain goal, such as reaching a weight loss target or attending the gym a specified

number of times in a week or month. This strategy allows individuals to create monetary

incentives for themselves and leverage loss aversion simultaneously. Volpp et al. (2008)

also find that a commitment contract (in which committed funds were matched 1-to-1 by

researchers to increase take up) was effective at achieving weight lost by study

participants.

One concern with monetary incentives of any form is that they may generate

short-term effects. Once the monetary incentives are removed, individuals may backslide

into their unhealthy behaviors. In Volpp et al. (2008) once the regret lottery or

commitment contract was removed, both groups regained a significant amount of the

weight they had lost during the study. John et al. (2011) run a longer study in which some

subjects have a commitment contract to lose weight (also with a 1-to-1 match) for a

period of 24 weeks. They find that subjects with the commitment contract lose

significantly more weight but much of the weight is regained in the 8 weeks after the

incentive is removed. At the end of those 8 weeks, the weight of the group that previously

had the contract was no different from the control group that never had the contract.

On the other hand, Giné et al. (2010) find that the effects of a six-month

commitment contract on smoking cessation can persist over the long run. In particular,

they find that smokers who were randomly offered the contract were significantly more

likely to pass a urine test for nicotine and cotinine (byproducts of tobacco use), and they

remained significantly more likely to pass another (surprise) urine test administered six

months after the removal of the incentive.

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There are other ways in which incentives can be altered to take advantage of

forces uncovered by Behavioral Economics. One example is to bundle a something

desirable (or addictive) with a behavior you want to encourage. The old adage “a

spoonful of sugar helps the medicine go down” can help explain why some vitamins are

candy-coated. In this spirit, Milkman et al. (2013) developed an incentive for gym

attendance using an activity that they deemed to be desirable and possibly addictive. In

their study, researchers provided each subject in their treatment groups with a loaner-iPod

loaded with popular books-on-tape that were considered relatively addictive (e.g. The

Hunger Games and The Da Vinci Code). For one treatment group, the iPods were stored

in lockers at the gym and could only be accessed if the subject was at the gym. The

authors found a significant increase in gym attendance in this group over the control

group, but also found that the effect deteriorated over time.

Crowding Out

Researchers in a number of disciplines across the social sciences have uncovered

some perverse effects of incentives. In particular, financial incentives have been shown at

times to decrease effort rather than increase it. This phenomenon has been called

Box 1: Behavioral Economics in Action Behavioural Insights Team (BIT) Established in 2010 by U.K. Prime Minister David Cameron, the BIT (nicknamed the “Nudge Unit”) uses insights from Behavioral Economics and Psychology in developing and testing different public policy measures. For example, inspired in part by Giné et al. (2010), the BIT is considering using loss-aversion and commitment contracts in their countrywide efforts to reduce smoking (BIT 2010). StickK.com The brainchild of several economists, StickK.com is a website that allows individuals to create and enter into their own commitment contracts to help them achieve personal goals. Common personal goals include losing weight, exercising regularly, and quitting smoking. Individuals determine how much money, if any, to put at stake and designate another individual to verify the goal outcome. Should an individual fail to reach his or her goal, the forfeited money goes to a charity of their choice or to an “anti-charity” that the individual does not want to support, making failure to meet the commitment feel even more costly.

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crowding out of intrinsic motivation as the monetary incentive is thought to replace the

intrinsic motivation to engage in an action (see Gneezy, Meier, and Rey-Biel 2011 for a

summary).

For example, Gneezy and Rustichini (2000a,b) find a number of crowding out

results in domains outside of health. They find that paying people a small amount for

each correct IQ problem solved can lead to fewer solved problems than if no monetary

incentive is in place. They also find that paying money to volunteers (e.g. 1% or 10% of

the amount of money collected while door-to-door fundraising) can decrease the amount

of money volunteers collect. Finally, imposing a monetary fine for being late to pick up

children from daycare led parents to be late more often rather than less. Similarly, it has

been shown that paying people for a short while to take a certain action and then

removing the incentive can lead individuals to provide less effort than before the

incentive was introduced (Deci 1971).

In the context of medical donation, Titmuss (1971) argued that providing a

monetary incentive for blood donation might decrease the amount of blood received.

Mellstrom and Johannesson (2008) find some weak evidence of this crowding out in

blood donation in response to an incentive worth about $7. In their experiment, they offer

the incentive to subjects to complete a health examination that was required to become a

blood donor. In one treatment the incentive could only be taken in cash while in another

treatment the subjects had the option to have the money donated to charity. They do not

see crowding out in response to the incentive on average, but they do see it among

women. Interestingly, crowding out is only present when the incentive must be taken in

cash and donation rates return to the control treatment levels when the money can be

donated to charity.

Discussion

One disadvantage of financial interventions is that pay-for-performance schemes

like providing monetary incentives to engage in health behavior are viewed by many as

unfair or unethical (see, e.g. Long et al. 2008). There may be a way to mitigate this

concern by making the incentives non-monetary. In the context of blood donation,

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individuals report a larger willingness to donate for a 10-euro voucher (to purchase books

or food) rather than for 10 euros in cash (Lacetera and Macis 2010).

A seen throughout this section, another potential disadvantage of reward

incentives is that they may fail to build enduring habits. While very effective when in

place, monetary incentives regularly fail to motivate continued behavior change after they

are removed. Only a few studies we discussed — Volpp et al. (2009), Charness and

Gneezy (2009), Giné et al. (2010), and Acland and Levy (2011) — found evidence that

paying subjects to engage in a health behavior had a lasting impact. In addition, Acland

and Levy (2011) suggest that an exogenous break in attendance (a semester break for

students) eliminated this effect. One solution to this problem is to keep monetary

incentives in place indefinitely, a possibility using variation in premiums allowed under

the Patient Protection and Affordable Care Act (Volpp et al. 2011).

The inability for monetary incentives to lead to sustained behavior change may be

in part a function of the potential crowding out effects of such incentives, although there

is still much work to be done to better understand crowding out effects and habit

formation in health behaviors.

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IV. Salience and Information

An assumption made by standard economic theory is that individuals have no

limitation in their ability to make decisions, either in terms of the cognitive capacity to

solve complex problems or the amount of time needed to do so.

To illustrate, imagine an individual who enters a store to purchase groceries.

According to standard economic theory, this individual considers all the goods he expects

to be available for purchase in the store (including all their prices or, if the prices are too

costly to discover, what he expects their prices to be) and calculates which basket of

goods would be best for him to buy. In doing so the individual also considers items in

others stores that he could potentially buy instead, factoring in the time and cost it would

take to drive to those stores to continue his shopping.

In reality, we have finite mental resources available to make our decisions so we

are prone to ignore much of the information available and take shortcuts in aggregating

what information we do attend to. An actual individual walking into the grocery store

might forget to pick up his vitamins, be distracted by a prominent display of candy, or

simply not realize that a healthier snack alternative is available on a bottom shelf.

Another way to put this is that individuals are boundedly rational. Simon (1957)

suggested that because individuals are limited by their cognitive capacity, as well as by

information and time constraints, they lack the resources to make a truly optimal

decision. Any decisions made by a boundedly rational individual are at best optimal

within his or her constraints.

Bounded rationality can influence individual behavior in several ways. For

instance, individuals may exhibit forgetfulness (i.e. limited memory), may fail to pay

attention (i.e. inattention), and may make decisions without collecting all the relevant

information available (i.e. imperfect information). We address these three phenomena

and their applications to health behaviors in turn below.

Limited Memory

No one likes to forget things. During the course of a day, however, many different

pieces of information are presented to us, and we must exert mental energy to remember

the important ones.

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In the health realm, this limited ability to perfectly store and process information

can help explain the difficulty many individuals have in adhering to a prescribed course

of treatment. One potential explanation for poor medication adherence is that individuals

are forgetful. Several studies have investigated interventions designed to remind people

to take their medication. In theory, the ideal intervention require no additional cognitive

resources.

To this end, electronic reminders are increasingly being used to address poor

medication adherence rates. These reminders alert individuals by either providing an

audial or visual reminder or by sending an automatic electronic message such as a text

message. Electronic reminders present two distinct advantages over other types of

reminders. First, these electronic reminders actively remind individuals to take their

medication. In contrast, passive reminders such as day-of-the-week pillboxes or blister

packaging provide information that medication should be taken but are not helpful

reminders if they are not seen. Active reminders are better suited for individuals whose

main reason for poor adherence is forgetfulness whereas passive reminders assist

individuals who need help remembering the correct dosage and combination of

medication when it is taken. Second, these reminders can be automated to arrive at a

specific time. For example, if an individual is liable to forget to take his medication, a

reminder provided when he is not with his pills might also be forgotten and fail to

improve adherence. Third, these reminders are relatively inexpensive because they can be

automated. Alternative reminders such as personal phone calls can increase adherence but

often require costly time investment and commitment by health care providers.

Vervloet et al. (2012) review thirteen randomized control trials that test the

effectiveness of different electronic reminders on adherence to various types of chronic

medication. They find evidence suggesting that electronic reminders encourage

medication adherence in the short-run (less than 6 months) but are less effective over the

long run. Furthermore, effectiveness can vary across medications for a given type of

electronic reminder. While text messages are an effective tool for increasing adherence

among adult patients with HIV (Hardy et al. 2011, Pop-Eleches et al. 2011) or children

requiring influenza vaccinations (Stockwell et al. 2012), they have mixed effects on

adherence for women taking oral contraceptives (Hou et al. 2010, Castano et al. 2012).

19

Forgetfulness can influence other health decisions as well. A large number of

preventative care measures require individuals to remember not only to set up an

appointment but also to follow through with it. The Centers for Disease Control and

Prevention recommend numerous preventative screening measures ranging from checks

for high blood pressure to diabetes screenings. Many individuals fail to take the

recommended preventative care measures, even when the monetary costs of doing so are

effectively zero. Forgetfulness may help to explain why individuals fail to take these

recommended preventative care measures.

The difficulty in remembering to engage in recommended health behaviors has

prompted both individuals and employers to seek out ways to outsource the task of

remembering. The company Evive Health, LLC keeps track of when the employees of

their clients are due for preventative screenings and sends personalized reminders at the

appropriate times, encouraging employees to make appointments and providing

information on how to do so. That employers are willing to pay companies such as Evive

highlights the importance of these behavioral consequences on health outcomes.

Inattention

The salience of a given piece of information, or the degree to which that piece of

information stands out relative to other information, can affect whether an individual

considers it in her decision-making process. If a piece of information is not particularly

salient, it might be overlooked or more easily forgotten. Consider two individuals who

must remember to take their medication. The first individual has a toothache and

experiences acute pain when he forgets his medication. The second individual suffers

from high cholesterol and does not discern any noticeable difference in discomfort if he

forgets. Because the pain of forgetting is more salient for the individual with a toothache,

he may more likely to remember his medication than the individual with high cholesterol.

In this case, the salience of individual health symptoms can influence whether or not an

individual remembers to take his medication.

Salience may also play a role by affecting the actual act of remembering. A

person who programs a reminder into their calendar for their next doctor’s appointment is

effectively making that appointment more salient. Milkman et al. (2011) design a field

20

experiment to test whether suggesting individuals write down when they intend to get an

influenza vaccine (i.e. making it more salient) increases rates of vaccination. The authors

study the behavior of employees who receive a reminder mailing that provides

information on the times and locations of free on-site vaccination clinics. Treated

subjects were randomly assigned to receive a prompt to write down either (1) the date the

employee plans to get vaccinated or (2) both the date and the time the employee plans to

get vaccinated. The authors find that the vaccination rate for employees with the date and

time prompt was significantly higher than for the control group that was not prompted to

write down anything. The rate for employees with the date only prompt was directionally,

but not significantly, higher than the control group. These results suggest that

encouraging individuals to make a more concrete plan — making the plan more salient

— can increase the likelihood of compliance.

Imperfect Information

Regardless of whether a person is rational or is subject to behavioral biases,

information is necessary to make the optimal decision. Consequently, decisions often

change as more information becomes available. Bounded rationality can influence this

process if it prevents individuals from optimally gathering or aggregating information.

In the past two decades, a growing literature has emerged looking at the effects of

providing nutritional or caloric information on nutrient intake, the consumption of food,

and other health outcomes. Such information could affect behavior if individuals do not

otherwise have all the information the need to make their food consumption decisions.

Several papers have studied the impact of the Nutrition Labeling and Education

Act (NLEA), which mandated the nutrition labeling of all pre-packaged foods in the U.S.

beginning in 1994. Prior to the NLEA, nutritional labeling was voluntary with the

exception of products that contained added nutrients or made nutritional claims. These

studies find that the enactment of the NLEA was associated with decreased body weight

(for some groups) and lower probability of obesity (Variyam and Cawley 2006),

increased fiber and iron intake (Variyam 2007), and lower calorie intake (Abaluck 2011;

Kim et al. 2000).

21

Other papers in the literature have instead focused on the provision of information

on the calorie content of foods. Several cities in the U.S., such as New York and

Philadelphia, currently require chain restaurants to post the caloric content of their menu

items. These laws are in part a reaction to the concern that individuals may have

imperfect information as to the true nutritional value of the items they purchase, which

may in turn lead to sub-optimal food consumption. Wisdom et al. (2010) conducted a

field experiment at a fast-food sandwich chain to study the effects of providing calorie

information. They find that total calorie consumption significantly decreases by sixty

calories on average when information on calorie content is provided. Wisdom et al.

(2012) find that making healthier sandwiches a more convenient choice relative to less

healthy sandwiches has significant decreases on total calorie consumption but only when

the intervention is relatively heavy-handed. While these findings suggest that consumers

do in fact change their consumption decisions when given more information, evidence

from other studies is more mixed (Bollinger et al. 2011; Elbel et al. 2009; Finkelstein et

al. 2011). In addition, many of these studies are limited by the fact that individuals who

are induced to consume less due to an experimental treatment may consume more at a

later meal when the experimenter can no longer observe them.

Discussion

The extent to which we care that individuals are boundedly rational depends on

whether (and by how much) individuals are made worse off because of their cognitive

limitations. In the realm of health care, the cognitive limitations faced by boundedly

rational individuals might have important ramifications for health and their overall

wellbeing.

Despite the research in this field, a great deal remains unknown about bounded

rationality in the health domain and how to best address cognitive resource limitations.

We do not yet understand in which settings the behaviors will be most affected by

bounded rationality. For example, the likelihood of forgetting (and thus the effectiveness

of reminders) may depend on the size of the costs to non-adherence. On one hand, the

likelihood of forgetting to take a medication may be higher when a disease is serious and

an individual does not want to think about it — suggesting reminders may be most

22

valuable when the consequences of non-adherence are relatively large. Alternatively, the

likelihood of forgetting might be higher when the stakes are lower — suggesting

reminders will help most when the consequences of non-adherence are small.

Additionally, it is hard to trace back from a given behavior to a particular

behavioral bias or cognitive limitation, which makes it hard to identify what intervention

will be most effective. For example, an individual may fail to make mammogram

appointment not because she is forgetful but instead because the costs associated with

figuring out where to make an appointment are too great, or because she lacks full

information on the risks of breast cancer, or because she perpetually plans to do it

tomorrow. Knowing how to most successfully impact behavior requires a better

understanding of its underlying cause.

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V. Context and Framing

According to standard economic theory, rational individuals make the optimal

choice given the set of options available and the constraints they face. The way in which

options are presented does not influence the decision made by a rational agent. For

example, a rational individual choosing between two different types of over-the-counter

pain relievers is not influenced by the order in which he sees the pain relievers when

walking down the drug store aisle. Likewise, a rational individual deciding whether to

enroll in one of three health care plans is not influenced by the fact that his employer

defaulted him into one of them.

If an individual behaves as standard economic theory predicts, her choices reflect

her true preferences. But Behavioral Economics suggests that context and framing can

play an important role in subtly influencing how we make decisions. Evidence of this

influence exists across a wide range of domains, including many high-stakes

environments where one might think individuals would be properly motivated to

implement their optimal choices. In this section, we discuss three examples of how

individuals are influenced by context and framing. First, individuals demonstrate a strong

tendency to exhibit inertia around default options. Second, individuals are easily

influenced by visual cues. Third, individual decisions depend on the manner in which the

choice set is presented or framed. Understanding the contextual influences to which

individuals are susceptible is critical for understanding individual behavior and for

effectively designing behavioral interventions.

Default Effects

One example of the effect of context on behavior is the strong tendency of

individuals to exhibit inertia around default options. Specifically, individuals tend to

remain with their default choice even in cases where the default is randomly assigned.

This default effect is well documented in empirical work across many different choice

settings, from retirement savings decisions (Carroll et al. 2009; Choi et al. 2004; Madrian

and Shea 2001; Samuelson and Zeckhauser 1988) to prescription drug home delivery

enrollment (Beshears et al. 2012) to Internet privacy agreements (Bellman et al. 2001,

2004).

24

In a classic study on default effects outside of the health domain, Johnson et al.

(1993)  investigate whether individuals remain in the auto insurance plan into which they

were defaulted. The authors were motivated by evidence from choices in the 1990s when

the states of New Jersey and Pennsylvania introduced auto insurance plans with lower

rates but limited rights to sue. Drivers in Pennsylvania were defaulted into the more

expensive “full right” plan whereas drivers in the neighboring state of New Jersey were

defaulted into the less expensive “limited right” plan. Because the underlying choices —

the full right plan or the limited right plan — were the same, whether drivers were

defaulted into one plan or the other was not expected to impact behavior. However, a

reported 75 percent of Pennsylvania drivers stayed with their “full right” plan whereas

only 20 percent of New Jersey drivers switched to the “full right” plan.

Defaults play an important role in many health domains, including insurance plan

choice. For example, people who remain eligible for Medicare Part D are defaulted into

their prescriptive drug coverage plan from the previous year. Ericson (2012) shows that

few enrollees switch from their plan from the previous year, even when relative prices of

plans change. Likewise, Handel (2011) looks at the behavior of enrollees in a PPO health

insurance plan after relative prices of plans changed significantly. As a result of the price

changes, the current plan of some enrollees became strictly dominated by an alternative

PPO health insurance plan (that is, for any possible level and type of medical

expenditure, their current plan would require higher combined premium and out of

pocket costs than the alternative PPO plan). Despite these enrollees’ plans being strictly

dominated, Handel finds that 89 percent of the enrollees choose the default: to remain in

their current plan. Default effects can similarly be found in employee contributions to

health-care flexible spending accounts (Schweitzer et al. 1996).

Default rules often affect whether individuals participate in certain programs.

Default rules generally either require explicit consent (e.g. an opt-in rule) or presume

consent (e.g. an opt-out rule). In the United States, deceased organ donation follows an

opt-in rule, requiring explicit consent from the deceased (e.g. having previously joined a

state registry) or consent from the deceased’s next-of-kin. But not all countries follow an

opt-in rule. A number of European countries follow an opt-out rule where individuals are

automatically assumed to be a donor unless they previously chose to remove themselves

25

from the registry. So long as the costs to opting in or opting out are small, the default rule

should have little influence on donor registration rates. Johnson and Goldstein (2003,

2004) show, however, that there is considerable variation in organ donor registration rates

by the default rule. They report that at the time of their research the effective consent rate

was around 17 percent in the United Kingdom, which followed an opt-in rule, and was 98

percent in Belgium, which followed an opt-out rule. Of the countries surveyed by the

authors, the difference in participation rate between the highest opt-in and lowest opt-out

countries was nearly 60 percentage points.

In a corresponding hypothetical choice experiment about organ donation, Johnson

and Goldstein randomly assigned individuals to one of three default conditions: (1) opt-

in, (2) opt-out, and (3) neutral. Under the third condition, individuals were not defaulted

into or out of the donor pool but were instead asked to make an “active choice” between

either donating or not donating. They find that the average participation rate by

individuals under the opt-out condition is significantly higher than that of individuals

under the opt-in condition. Johnson and Goldstein also find that the neutral condition is

not significantly different from the opt-out condition, suggesting that asking individuals

to make an active choice might be more effective at generating organ donor registrations

than the opt-in policy. Similar arguments about organ donation are made in the popular

press (Thaler 2009). The United Kingdom and a number of U.S. states (including,

notably: Illinois, California, and New York) have passed legislation — some of which

has already been implemented — to change the request for organ donor registration from

opt-in to active choice.

It is too soon to tell whether these changes will increase organ donor registration

rates. In addition, experimental evidence on real organ donor registrations finds that the

active choice frame does not improve registration rates over an opt-in frame, and may

lead to lower consent rates from next-of-kin (Kessler and Roth 2013).

There are a number of explanations for why individuals tend to exhibit inertia

around default options. Traditional economic theory suggests that the default effect could

be accounted for by the presence of switching or transaction costs. These costs do not

have to be monetary in nature but can also include time and effort that individuals have to

expend to switch away from the default. If the costs to switching are large enough,

26

individuals will actually prefer not to switch from their default option. Inertia around

default options could also be rational behavior if individuals believe the default option is

an implicit recommendation by the person who chose the default. For example, an

individual may view a default health plan choice as an implicit recommendation from her

employer.

Default effects are often present even when the apparent costs to switching are

negligible and the likelihood of the default being viewed as an implicit recommendation

is small. An alternative behavioral explanation for such an effect is the bounded

rationality of individuals. For example, an individual may be enrolled in the same

prescription drug plan as the previous year (the default option) because after the initial

enrollment period, he paid little attention to any changes in circumstances that might

induce him to switch. Because of this inattention, the individual fails to make a conscious

choice and so is automatically enrolled into his plan from the previous year.

Alternatively, people may simply exhibit status quo bias, a preference for the current

status quo independent of whatever the best option is, or omission bias, a preference for

inaction over action. Any combination of these explanations could lead to inaction and

thus the observed default effect. Regardless of what the true underlying mechanism is,

however, it remains that defaults can have a significant influence on individual choices.

Visual Cues

Visual cues and other environmental factors can also play a role in individual

decision-making. Bernartzi and Thaler (2007) find that the number of lines displayed on

an investment elections form influenced the number of funds into which individuals

invested their retirement savings. Individuals were randomly given either a form with

eight lines or a form with only four lines (but a nearly costless way to increase the

number of lines). Of the individuals given the four-line form, only 10 percent invested in

more than four funds. By comparison, approximately 40 percent of individuals given the

eight-line form invested in more than four funds.

Within the realm of health, there is an extensive literature looking at how visual

cues can affect food consumption and food choices. Everything from the visibility or

salience of food to the size and shape of food packaging can influence individual

27

consumption. For example, people consume more food when they are given larger

serving bowls (Wansink and Cheney 2005) or there is greater perceived variety (Rolls et

al. 1981, Kahn and Wansink 2004). They pour 30 percent more alcohol when using short,

wide glasses rather than tall, narrow glasses (Wansink and van Ittersum 2005).

The environment in which we eat can also affect food consumption choices. An

individual purchasing groceries may be more likely to buy tempting goods, such as candy

bars, if they are located near the register where the effort cost of adding them to the

purchase is low than if they are located at the opposite end of the store. In this example,

the convenience of the candy bars may influence the likelihood of purchase. Hanks et al.

(2012) study whether the conversion of a cafeteria lunch line into a “convenience line”

that only offered healthy food options influenced the consumption of healthy (versus

unhealthy) foods. The authors find that following the introduction of a convenience line,

the consumption (measured in grams) of healthy foods chosen did not change but the

consumption of unhealthy foods decreased significantly by 27.9 percent. The share of

total consumption from healthy foods increased on average while the share from

unhealthy foods decreased.

Framing

Another assumption about rational economic actors is that their preferences are

consistent. This consistency implies that the way a set of choices is framed or presented

does not influence the individual’s decision so long as there is no additional information

is conveyed by the frame.

In a famous example, Tversky and Kahneman (1981) demonstrated that framing

affects choices using a hypothetical life-or-death scenario. Their study asked participants

to consider a scenario where the U.S. is preparing for the outbreak of a disease, which is

expected to kill 600 people. There are two proposed programs to combat the disease. The

first set of respondents to the survey were told:

• If Program A is adopted, 200 people will be saved

• If Program B is adopted, there is 1/3 probability that 600 people will be saved,

and 2/3 probability that no people will be saved.

The second set of respondents were instead told:

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• If Program C is adopted, 400 people will die.

• If Program D is adopted, there is 1/3 probability that nobody will die, and 2/3

probability that 600 people will die.

Both groups were then asked which of the two programs they would favor. The first

program for each group involves no risk (the outcome is presented with certainty)

whereas the second program involves some risk taking. Of the respondents in the first

group, 72 percent chose Program A over Program B. Of the respondents in the second

group, 78 percent chose Program D over Program C. The authors point out, however, that

Program A and Program C are equivalent, as are Program B and Program D.

When the estimates of the consequences of each of the programs were framed in

terms of lives saved, respondents preferred the program that offered the certain outcome.

Yet when the consequences were instead framed in terms of lives lost, respondents

preferred the program that involved risk taking. As it turns out, in situations involving

risk individuals often exhibit such preference reversals depending on whether the

outcome is framed in terms of gains or losses. Individuals are generally risk averse when

facing gains and risk seeking when facing losses (Kahneman and Tversky 1979).

While the proposed scenario of a disease outbreak was hypothetical, such framing

effects have been shown to have consequences for individuals making real decisions

regarding their health. McNiel et al. (1982) find that individuals are more likely to prefer

surgery to radiation therapy (which has no risk of death during treatment) when told that

surgeries face a 90 percent survival rate as opposed to a 10 percent mortality rate. In

another study, women’s attitudes towards potential side effects of tamoxifen, a

medication taken by high-risk women to help prevent the first-time development of breast

cancer, varied depending on whether they were informed that 17 out of 100 women

experience cataracts as a side effect versus 170 out of 1000 women (Zikmund-Fisher et

al. 2008). While the information presented is the same in either case, participants were

more concerned about the potential side effects when randomly informed of the risk

statistic using the larger denominator. The way in which this information is framed can

significantly affect the decision-making process.

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Discussion

Recognizing the influence that context and framing can have on individual

behavior is important, especially when the choices lead to significantly different

outcomes — some of which might make individuals better off. It is often difficult to

separately identify the effects of context and framing from other factors that may come

into play, and so any estimates of the effects of these cues on behavior and welfare must

be considered carefully. Even with this caveat, however, it is clear that context and

framing can significantly influence individual choices and outcomes. For example,

Handel (2011) finds that, on average, individuals who are defaulted into their health plan

choice from the previous year (but are given an option to switch) forgo roughly $2000 in

implied savings by staying in their default plan rather than choosing an alternative option.

If individuals choose to stay in the same plan because they were defaulted into it, rather

than due to switching costs, the decision can represent a substantial loss to individuals.

Furthermore, if defaults lead to low levels of switching, the factors that influence

individuals in their initial decision can end up having long-term effects.

One way to reduce the influence of default effects, visual cues, and framing is to

help individuals overcome these biases while making their decisions. For example,

providing information that lessens the complexity of a decision can decrease default

effects. Enrollees in prescription drug plans who are provided with information regarding

the relative costs of each of the available plans are much more likely to switch from their

current plan (the default) than those who do not receive such information (Kling et al.

2012).

Similarly, rather than requiring individuals to either opt in or opt out of

participation or enrollment in a program, an alternative solution is to require that these

individuals actively consider the decision being made. This can be achieved either by

designing the choice environment so that individuals are forced to consider whether they

really want to opt in (or out) or by requiring that they make an active choice.

30

It is sometimes difficult or impossible, however, to design an environment that

does not influence behaviors in some way. And in many scenarios involving public

health, it may actually preferable to use behavioral economics to design choice

environments so as to encourage certain behaviors. For example, it is extremely difficult

to design a cafeteria environment that does not influence food consumption decisions.

Food items must be placed in some order and in some location, and so influencing food

consumption is unavoidable. Often the current design of an environment is simply one

potential design option (just the one into which the policy maker has been defaulted).

Although contextual influence has long been recognized, Thaler and Sunstein

(2008) popularized the topic with their discussion of choice architecture — the design of

the environments in which individuals make choices. As they note, Behavioral

Economics can be a useful tool in nudging individuals towards behaviors that are

beneficial while still allowing individuals complete autonomy in making their choice. A

choice architect may choose to use an opt-out rule when there is underutilization of

something seen as beneficial. For example, influenza vaccinations are often encouraged

in part because individuals who do not receive the vaccination impose a negative

Box 2: Behavioral Economics and Lessons for Regulatory Policy Cass R. Sunstein, co-author of Nudge (2008) and Administrator of the White House Office of Information Regulatory Affairs (2009-2012), suggests four promising approaches to regulatory policy based on the insights of Behavioral Economics:

1) Using disclosure as a regulatory tool, especially if disclosure policies are designed with an appreciation of how people process information.

2) Simplifying and easing choices through appropriate default rules, reduction of complexity and paperwork requirements and related strategies.

3) Increasing the salience of certain factors or variables. 4) Promoting social norms through private-public partnerships and other

approaches that operate in the service of agreed-upon public goals. These approaches address some of the commonly cited behavioral biases studied in Behavioral Economics and mentioned in this chapter. Source: Sunstein (Forthcoming).

31

externality on other individuals. In such instances, designing a behavioral intervention

where individuals must opt-out of receiving a vaccination can increase overall

vaccination rates (Chapman et al. 2010; Keller et al. 2011). Similarly, a school may

design the food display in their cafeteria so that salad options are placed before dessert

options, and an employer may encourage savings by requiring individuals to opt out of

contributing to a savings plan.

32

VI. Social Forces

Humans are social animals who mirror the actions of others, behave differently

when they know that others will learn about their actions, and respond to requests. These

features of human behavior suggest three predictions. First, individuals informed about

the actions of others will tend to conform to others’ behavior. Second, individuals who

become aware that they are being observed will take actions that make them appear more

favorably in the eyes of others (e.g. they will take actions that make them look more

generous or more responsible). Third, individuals will be more likely to take an action

when asked to do so by someone else. These social forces — which can be partially

justified by the rational model and which we think of partially as behavioral — influence

a wide variety of behaviors, including a number in the health domain. For each of the

three sections below, we provide evidence from health and non-health settings. As will be

discussed at the end of this section, we believe social forces to be an area of behavioral

health economics that has the opportunity to grow in the coming years.

Responding to the Actions of Others

It has been well documented that individuals informed about the actions of others

tend to conform to others’ behavior. One way in which researchers have established this

fact is to provide experimental subjects with social information: specific information

about the actions of other people. Individuals who are provided with such information

respond by taking actions that are more similar to the behavior of others.

Outside of the health domain, this effect of social information has been observed

in settings including charitable giving, environmental protection, and job choice.

Individuals are more likely to donate to charity when they are told that others also donate.

Frey and Meier (2004) find that students are more likely to make charitable donations to

student funds at the University of Zurich when they are told that 64 percent of students

donate to the funds (64 percent was a recent semester’s average) rather than being told

that 46 percent of students donate to the funds (where 46 percent was a 10-year average).

Individuals also donate more money to a charity when they are told that others donated

larger amounts. Shang and Croson (2009) find that the amount of money donated to a

public radio campaign increases when donors who have called in to make a gift are told

33

of a large recent donation of $300 rather than being told of a smaller donation of $75 or

being told no information about a previous donation. This effect on donation amount

works even more strongly in the negative direction; people donated significantly less

when the donation amount cited is below what they gave in the previous year (Croson

and Shang 2008). Individuals are also more likely to engage in environmental protection

when they are told that others engage in environmental protection. In a study on towel

reuse, Goldstein, Cialdini and Griskevicius (2008) find that hotel guests are significantly

more likely to reuse their towel — saving the water and electricity required to wash it —

when they are told that 75 percent of hotel guests reuse their towels rather than being

given a generic environmental appeal. As with the charity examples, information can also

impact the extent to which an individual engages in environmental protection. Allcott

(2011) finds that sending households report cards comparing their electricity use to the

electricity use of similarly sized households can decrease electricity usage by as much as

an 11 to 20 percent short-run price increase. On job choice, Coffman, Featherstone and

Kessler (2013) find that individuals are more likely to accept a job as a teacher in an

underfunded public school when they are told that 84 percent of accepted applicants took

the job in the previous year.

That individuals respond to the actions of others has been shown to be important

in the health domain as well. Here we focus on binge drinking and smoking, contexts in

which individuals have been shown to conform to the behaviors of those around them.

Most students across the country have beliefs about the drinking habits of other

students that are “too high” — they think their peers drink more heavily than their peers

actually do. Researchers have shown that beliefs about peer alcohol consumption

correlate with own alcohol consumption. Perkins, Haines and Rice (2005) perform a

multivariate analysis of survey data from 130 schools and find overestimates of alcohol

consumption among peers and a strong statistical relationship between those beliefs and

own alcohol consumption, controlling for all available demographic variables. A similar

approach has found a correlation between the smoking behavior of adolescents and their

perceptions of other teens smoking (Eisenberg and Forster 2003).

This correlational evidence is suggestive of a causal link in which individuals

respond directly to their beliefs about other’s drinking or smoking habits when making

34

their own decision of whether or not to drink or smoke. This correlational evidence,

however, is far from conclusive. It is quite possible that the causal link goes in the

opposite direction. For example, teens may drink or smoke in groups; those who smoke

may be more likely to observe others drinking and smoking and thus generate estimates

about the prevalence of those behaviors among the rest of their peers that are too high.

Similarly reported beliefs about others’ drinking may be influenced by own drinking and

the assumption that others are similar to oneself, leading those who drink or smoke more

to believe that others drink or smoke more as well.

A true experiment that manipulates the information provided to individuals could

more successfully show a causal link. For the case of alcohol consumption, DeJong et al.

(2006) provide this experimental demonstration in a study involving 18 institutions of

higher learning across which they randomized whether the schools ran a three-year social

norms marketing campaign (SNMC). These SNMCs educated college students about the

actual alcohol consumption of students at the school. Many colleges and universities had

previously implemented these types of SNMCs to inform students of the actual drinking

behaviors of their students and so one aim of the research was to experimentally test their

effectiveness.

The authors gathered self-reported beliefs about the alcohol consumption of other

students and the self-reported own consumption from the Survey of College Alcohol

Norms and Behavior conducted before and after the three-year SNMCs. Students

surveyed at schools that were randomly selected to have a campaign reported larger

decreases in drinking from pre-survey to post-survey on a number of dimensions

(including a composite drinking scale, recent maximum consumption, and drinks

consumed when partying). This decrease was associated with a decrease in reported

beliefs about the drinking of others at the school. The authors also note that the decrease

in drinking was more pronounced at schools with more intensive SNMCs.

When others are watching

Both inside and outside the health domain, individuals take actions that make

them look more generous and more responsible when they believe that they are being

35

observed. These effects are stronger when individuals care more about the person or

persons observing them.

Outside of the health domain, being observed affects voting, charitable giving,

and providing other public goods (i.e. taking costly actions that benefit others). On

voting, Gerber et al. (2008) show that informing individuals that whether or not they vote

is public record (and that their voting is being watched by researchers) increases the

likelihood of voting over a standard message. Voting rates are even higher when

individuals are told that their voting records will be sent to their neighbors after the

upcoming election. In charitable giving, Harbaugh (1998) demonstrates that individuals

care about what others learn about their donations. When a prestigious law school

switches reporting all donations to reporting donations by category, donations become

significantly more clustered at the lowest amount that achieves a certain level of

recognition (e.g. many more donations at $500 when the reported category is $500-$999).

In the health domain, image concerns affect individuals’ willingness to engage in

charitable health donation behaviors, including donating blood. Lacetera and Macis

(2010) look at donation records from an Italian town; they find an increase in likelihood

of blood donation as individuals approach the threshold for a symbolic prize (a medal) for

their donations, but only if the winners of the medals are announced in the local

newspaper and awarded publicly.

Combining social forces with monetary incentives can potentially strength the

effect of an intervention. Haisley et al. (2012) increase the percentage of employees who

complete a health risk assessment by combining a regret lottery (described in Section III)

with a social component where individuals could earn more if other employees — whose

identities they knew — also completed the assessment. This treatment worked better than

a standard reward incentive of a similar expected value.

Requests from others

Individuals are more likely to take an action when asked by some else to do so.

Encouragement or discouragement from others (also known as peer pressure) is a

powerful social force that can influence individual behavior inside and outside of health

contexts.

36

Outside of the health domain, Meer and Rosen (2011) show that being more likely

to be called on the phone in a university fundraising campaign makes donors much more

likely to make a donation. Similarly, DellaVigna et al. (2011) show that individuals are

less likely to answer to the door and less likely to make a donation in a door-to-door

fundraising campaign when they are warned that an individual is coming to make a

request. They interpret this result as individuals being worried about facing peer pressure

and choosing to opt out when they know they will be asked to donate. Freeman (1997)

reports that people are much more likely to volunteer when asked by a friend.

A number of health interventions attempt to leverage individuals’ favorable

responses to requests from others. Campbell et al. (2008) report on the results of a

randomized controlled trial on teen smoking using 59 schools and 10,730 students aged

12-13 in Wales. Control schools received the standard smoking education. Treated

schools had a special program that included peer mentors: students who were trained to

have informal conversations with their peers in which they discouraged smoking. Treated

schools showed a decrease in the likelihood that students smoked one year later. By the

second year after the intervention, the effect is attenuated (and no longer statistically

significant), although students at the treated schools are still directionally less likely to

smoke.

Kelly et al. (1991) report that training individuals identified as opinion leaders in

their communities to endorse safe sex among gay men at risk of HIV drastically

decreased the extent to which gay men engaged in unprotected anal intercourse (as

measured by surveys before and after the intervention). Control cities that did not have

individuals trained to endorse behavior change did not show a decrease in unsafe sex

during the same time period.

Long et al. (2012) investigate a peer mentoring treatment on glucose control for

individuals with diabetes. In one treatment, subjects with poor diabetes control are paired

with mentors who previously had poor control but have since improved. On average the

mentor-mentee pairs talk once a week at the start of the study and once every two weeks

at the end of the study 6 months later. The peer mentor group shows a large and

significant improvement in glucose control. The authors found that this group also

outperformed a financial incentive treatment where individuals were paid $100 or $200

37

for decreasing their glucose level. Similarly, the Geisinger Health System has been able

to achieve better patient outcomes by having nurses follow-up with patients to monitor

medication use and address possible questions and concerns (Cutler and Everett 2010).

Discussion

Social forces have been shown to have a powerful effect on behavior in a number

of domains outside of health, including charitable giving, environmental protection,

voting, and job choice. The growing evidence on how social forces impact behavior in

health domains suggests that these forces may be particularly effective in affecting health

behaviors as well.

If shown to be effective, social forces have a number of particular benefits in the

health domain. First, leveraging social forces can be significantly less expensive than

monetary incentives and thus may provide a very cost-effective way to affect behavior

(Hollingworth et al. 2012). Second, while we have yet to see many studies with long

follow-up periods to test for the effectiveness of social treatments in the long term, there

is reason to believe that social forces might build sustaining habits for healthy behavior.

Unlike a monetary incentive, which is either on or off, social incentives might have

lingering effects. Once someone has been told that fewer people drink that he suspected,

he may hold onto that information and it may still affect him months or years later. A

peer mentor might continue to provide mentorship even after a study intervention period

is over. In this way, social forces have the potential to provide longer-lasting effects.

Additional research is needed to investigate the sustainability of these forces — research

that we hope to see conducted in the coming years.

38

VII. Conclusion

This chapter outlines findings from Behavioral Economics, highlighting four topic

areas: reward incentives, salience and information, context and framing, and social

forces. Results from each of these topic areas suggest interventions that can affect health

behaviors.

As emphasized in the introduction, policy makers must be responsible with when

and how they use available interventions to affect health behaviors. Policy makers must

look for constraints, externalities, or behavioral biases to justify intervention. Fortunately,

some individuals with behavioral biases may explicitly ask for interventions. They may

recognize that they need help to engage in certain desirable behaviors like going to the

gym, eating healthy, or taking their medications. In these cases, individuals will happily

sign up for studies designed to test ways to impact behavior, allowing us to conduct more

research. In addition, they will happily opt into programs policy makers design to

effectively encourage behavior change.

Previous research has provided us with some insights into how to motivate

behavior, but exciting work remains to be done. One open question of particular

importance — the proverbial holy grail of Behavioral Economics research in health

behavior — is how to create habit formation among individuals who want to engage in

healthy behaviors and have trouble doing so. In this chapter, we saw examples in which a

treatment that was in place for a while and then removed might have created a habit for

visiting the gym, at least for a short while (Charness and Gneezy 2009, Acland and Levy

2011). In addition, we saw success in breaking the hold of an addictive behavior like

smoking (Volpp et al. 2009, Giné et al. 2010). But there are many more examples of

interventions failing to help subjects form healthy habits that persist after the intervention

is removed. By better understanding habit formation we can develop interventions that

can be cost effective and have significant policy appeal.

39

References

Abaluck J. What would we eat if we knew more: The implications of a large-scale change

in nutrition labeling. 2011 Oct 17 (Unpublished).

Acland, D. and Levy, M. Habit formation, naivete, and projection bias in gym attendance.

2011 Apr 13 (Unpublished).

Allcott H. Social norms and energy conservation. J Public Econ. 2011 Oct; 95(9-

10):1082-95.

Behavioural Insights Team: Applying behavioural insight to health [Internet]. London:

Crown; 2010 Dec [cited 2013 Apr 25]. Available from:

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/60

524/403936_BehaviouralInsight_acc.pdf

Bellman S, Johnson EJ, Lohse GL. To opt-in or opt-out? It depends on the question.

Commun ACM. 2001 Feb; 44(2): 25-7.

Bellman S, Johnson, EJ, Kobrin, SJ, et al. International differences in information privacy

concerns: A global survey of consumers. Inform Soc. 2004 Nov; 20(5):313-24.

Bernartzi S, Thaler RH. Heuristics and biases in retirement savings behavior. J Econ

Perspect. 2007; 21(3):81-104.

Beshears J, Choi JJ, Laibson D, et al. Active choice and health care costs: Evidence from

prescription drug home delivery. 2012 Oct 3 (Unpublished).

Bollinger B, Leslie P, Sorensen A. Calorie posting in chain restaurants. Am Econ J Econ

Policy. 2011 Feb; 3(1):91-128.

40

Campbell R, Starkey F, Holliday J, et al. An informal school-based peer-led intervention

for smoking prevention in adolescence (ASSIST): A cluster randomised trial.

Lancet. 2008 May; 371(9624):1595-1602.

Caroll GD, Choi JJ, Laibson D, et al. Optimal defaults and active decisions. Q J Econ.

2009; 124(4):1639-74.

Castano PM, Bynum JY, Andres R, et al. Effect of daily text messages on oral

contraceptive continuation: A randomized controlled trial. Obstet Gynecol. 2012

Jan; 119(1):14-20.

Chapman GB, Li M, Colby H, et al. Opting in vs opting out of influenza vaccination.

JAMA. 2010 Jul; 304(1):43-4.

Charness G, Gneezy U. Incentives to Exercise. Econometrica. 2009 May; 77(3):909-31.

Choi JJ, Laibson D, Madrian BC, et al. For better or worse: default effects and 401(k)

savings behavior. In: Wise DA, editor. Perspectives on the Economics of Aging.

1st ed. Chicago: University of Chicago Press; 2004.

Coffman L, Featherstone CR, Kessler JB. Do small interventions affect large decisions?

Experimental evidence from Teach for America. 2013 (Unpublished).

Croson R, Shang J. The impact of downward social information on contribution

decisions. Exp Econ. 2008; 11:221-33.

Cutler DM, Everett W. Thinking outside the pillbox—Medication adherence as a priority

for health care reform. N Engl J Med. 2010 Apr 29; 362(17):1553-5.

Deci EL. Effects of externally mediated rewards on intrinsic motivation. J Pers Soc

Psychol. 1971; 18(1): 105-15.

41

DellaVigna S, List JA, Malmendier U. Testing for altruism and social pressure in

charitable giving. Q J Econ. 2012; 127(1):1-56.

DellaVigna S, Malmendier U. Paying not to go to the gym. Am Econ Rev. 2006 Jun;

96(3):694-719.

DeJong W, Schneider SK, Towvim LG, et al. A multisite randomized trial of social

norms marketing campaigns to reduce college student drinking. J Stud Alcohol.

2006 Nov; 67(6):868-79.

Dew MA, Switzer GE, Goycoolea JM, et al. Does transplantation produce quality of life

benefits? A quantitative analysis of the literature. Transplantation. 1997 Nov 15;

64(9):1261-73.

Donate Life America 2012 Annual Update [Internet]. Richmond: Donate Life America;

2012 [cited 2013 Mar 24]. Available from: http://donatelife.net/wp-

content/uploads/2012/09/DLA-Annual-Update-2012-Final-35715_lowres-over-5-

mb.pdf

Doshi JA, Zhu J, Lee BY, et al. Impact of a prescription copayment increase on lipid-

lowering medication adherence in veterans. Circulation. 2009 Jan 27; 119(3):390-

7.

Eisenberg ME, Forster JL. Adolescent smoking behavior: Measures of social norms. Am

J Prev Med. 2003 Aug; 25(2):122-8.

Elbel B, Kersh R, Brescoll VL, et al. Calorie labeling and food choices: a first look at the

effects on low-income people in New York City. Health Aff (Millwood). 2009

Nov-Dec; 28(6):w1110-21.

42

Ericson KMM. Consumer inertia and firm pricing in the Medicare Part D prescription

drug insurance exchange. 2012 Sep; NBER Working Paper, No. 18359.

Ferrari JR, Barone RC, Jason LA, et al. The use of incentives to increase blood

donations. J Soc Psychol. 1985 Dec; 125(6):791-3.

Finkelstein EA, Strombotne KL, Chan NL, et al. Mandatory menu labeling in one fast-

food chain in King County, Washington. Am J Prev Med. 2011 Feb; 40(2):122-7.

Flegal KM, Caroll MD, Ogden CL, et al. Prevalence and trends in obesity among US

adults, 1999-2008. JAMA. 2010 Jan 20; 303(3):235-41.

Flegal KM, Graubard BI, Williamson DF, et al. Cause-specific excess deaths associated

with underweight, overweight, and obesity. JAMA. 2007 Nov 7; 298(17):2028-

37.

Freeman RB. Working for nothing: The supply of volunteer labor. J Labor Econ. 1997;

15(1):S140-66.

Frey BS, Meier S. Social comparisons and pro-social behavior: Testing “conditional

cooperation” in a field experiment. Am Econ Rev. 2004 Dec; 94(5):1717-22.

Gerber AS, Green DP, Larimer CW. Social pressure and voter turnout: evidence from a

large-scale field experiment. Am Polit Sci Rev. 2008 Feb; 102(1):22-48.

Giné X, Karlan D, Zinman J. Put your money where your butt is: A commitment contract

for smoking cessation. Am Econ J Appl Econ. 2010 Oct; 2(4):213-35.

Gneezy U, Rustichini A. Pay enough or don’t pay at all. Q J Econ. 2000a; 115(3):791-

810.

43

Gneezy U, Rustichini A. A fine is a price. J Legal Stud. 2000b Jan; 29(1):1-17.

Gneezy U, Meier S, Rey-Beil P. When and Why Incentives (Don’t) Work to Modify

Behavior. J Econ Perspect. 2011; 25(4):191-210.

Goette L, Stutzer A. Blood donations and incentives: Evidence from a field experiment.

2008 Jun; IZA Discussion Paper, No. 3580.

Goette L, Stutzer A, Frey BM. Prosocial motivation and blood donations: A survey of the

empirical literature. Transfus Med Hemother. 2010 Jun; 37(3):149-54.

Goldstein NJ, Cialdini RB, Griskevicius V. A room with a viewpoint: Using social norms

to motivate environmental conservation in hotels. J Consum Res. 2008 Aug;

35:472-82.

Haisley E, Volpp KG, Pellathy T, Loewenstein G. The impact of alternative incentive

schemes on completion of health risk assessments. American Journal of Health

Promotion. 2012 Jul; 26(3):184-8.

Handel B. Adverse Selection and Switching Costs in Health Insurance Markets: When

Nudging Hurts. 2011 Sep; NBER Working Paper, No. 17459.

Hanks AS, Just DR, Smith LE, et al. Healthy convenience: nudging students toward

healthier choices in the lunchroom. J Public Health. 2012 Aug; 34(3):370-6.

Harbaugh WT. What do donations buy? A model of philanthropy based on prestige and

warm glow. J Public Econ. 1998 Feb 1; 67(2):269-84.

Hardy H, Kumar V, Doros G, et al. Randomized controlled trial of a personalized cellular

phone reminder system to enhance adherence to antiretroviral therapy. AIDS

Patient Care STDS. 2011 Mar; 25(3):153-61.

44

Hollingworth W, Cohen D, Hawkins J, et al. Reducing smoking in adolescents: Cost-

effectiveness results from the cluster randomized ASSIST (A Stop Smoking In

Schools Trial). Nicotine Tob Res. 2012 Feb; 14(2):161-8.

Hou MY, Hurwitz S, Kavanagh E, et al. Using daily text-message reminders to improve

adherence with oral contraceptives: A randomized controlled trial. Obstet

Gynecol. 2010 Sep; 116(3):633-40.

John LK, Loewenstein G, Troxel AB, et al. Financial incentives for extended weight loss:

A randomized, controlled trial. J Gen Intern Med. 2011 Jun; 26(6):621-6.

Johnson EJ, Hershey J, Meszaros J, et al. Framing, probability distortions, and insurance

decisions. J Risk Uncertainty. 1993; 7:35-51.

Johnson EJ, Goldstein DG. Do defaults save lives? Science. 2003 Nov 21;

302(5649):1338-9.

Johnson EJ, Goldstein DG. Defaults and donation decisions. Transplantation. 2004;

78:1713-6.

Kahn BE, Wansink B. The influence of assortment structure on perceived variety and

consumption quantities. J Consum Res. 2004 Mar; 30(4):519-33.

Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk.

Econometrica. 1979 Mar; 47(2):263-92.

Karlan D, McConnell M, Mullainathan S, et al. Getting to the top of mind: How

reminders increase saving. 2010 Jul; NBER Working Paper, No. 16205.

45

Keller PA, Harlam B, Loewenstein G, et al. Enhanced active choice: A new method to

motivate behavior change. J Consum Psychol. 2011 Oct; 21(4):376-83.

Kelly JA, St. Lawrence JS, Diaz YE, et al. HIV risk behavior reduction following

intervention with key opinion leaders of population: an experimental analysis. Am

J Public Health. 1991 Feb; 81(2):168-71.

Kessler JB, Roth AE. Organ allocation policy and the decision to donate. American

Economic Review. 2012 Aug; 102(5):2018-47.

Kessler JB, Roth AE. Don’t take ‘No’ for an answer: An experiment with actual organ

donor registrations. 2013 (Unpublished).

Kessler JB, Roth AE. Organ Donation Loopholes Undermine Warm Glow Giving: An

Experiment Motivated By Priority Loopholes in Israel. 2013 (Unpublished).

Kim S, Nayga Jr. RM, Capps Jr. O. The effect of food label use on nutrient intakes: An

endogenous switching regression analysis. J Agr Resour Econ. 2000 Jul;

25(1):215–31.

Kling JR, Mullainathan S, Shafir E, et al. Comparison friction: Experimental evidence

from Medicare drug plans. Q J Econ. 2012; 127(1):199-235.

Lacetera N, Macis M. Social image concerns and prosocial behavior: Field evidence from

a nonlinear incentive scheme. J Econ Behav Organ. 2010 Nov; 76(2):225-37.

Lacetera N, Macis M. Do all material incentives for pro-social activities backfire? The

response to cash and non-cash incentives for blood donations. J Econ Psychol.

2010 Aug; 31(4):738-48.

46

Lacetera N, Macis M, Slonim R. Will there be blood? Incentives and displacement effects

in pro-social behavior. Am Econ J Econ Policy. 2012 Feb; 4(1):186-223.

Lacetera N, Macis M, Slonim R. Economic Rewards to Motivate Blood Donations.

Science. 2013 May; 340(6135):927-8.

Lacetera N, Macis M, Stith SS. Removing financing barriers to organ and bone marrow

donation: The effect of leave and tax legislation in the U.S. 2012. 2012 Aug;

NBER Working Paper, No. 18299.

Lavee J, Ashkenazi T, Stoler A, et al. Preliminary marked increase in the national organ

donation rate in Israel following implementation of a new organ transplantation

law. Am J Transplant. 2013 Mar; 13(3):780-5.

Long JA, Helweg-Larsen M, Volpp KG. Patient opinions regarding ‘pay for performance

for patients.’ J Gen Intern Med. 2008 Oct; 23(10):1647-52.

Long JA, Jahnie EC, Richardson DM, et al. Peer mentoring and financial incentives to

improve glucose control in African American veterans: A randomized trial. Ann

Intern Med. 2012 Mar 20; 156(6):416-24.

Madrian B, Shea DF. The power of suggestion: Inertia in 401(k) participation and

savings behavior. Q J Econ. 2001; 116(4):1149-525.

McNeil BJ, Pauker SG, Sox HC, et al. On the elicitation of preferences for alternative

therapies. N Engl J Med. 1982 May 27; 306(21):1259-62.

Meer J, Rosen HS. The ABCs of charitable solicitation. J Public Econ. 2011 Jun: 95(5-

6):363-71.

47

Mellstrom C, Johannesson M. Crowding out in blood donation: Was Titmuss right? J Eur

Econ Assoc. 2008 Jun; 6(4):845-63.

Milkman KL, Beshears J, Choi JJ, et al. Using implementation intentions prompts to

enhance influenza vaccination rates. Proc Natl Acad Sci USA. 2011 Jun 28;

108(26):10415-20.

Milkman KL, Minson JA, Volpp KG. Holding the Hunger Games hostage at the gym: An

evaluation of temptation bundling. 2013 Apr 3; The Wharton School Research

Paper, No. 45.

Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005 Aug 4;

353(5):487-97.

Perkins HW, Haines MP, Rice R. Misperceiving the college drinking norm and related

problems: A nationwide study of exposure to prevention information, perceived

norms and student alcohol misuse. J Stud Alcohol. 2005 Jul; 66(4):470-8.

Pop-Eleches C, Thirumurthy H, Habyarimana JP, et al. Mobile phone technologies

improve adherence to antiretroviral treatment in a resource-limited setting: A

randomized controlled trial of text message reminders. AIDS. 2011 Mar 27;

25(6):825-34.

Rolls BJ, Rowe EA, Rolls ET, et al. Variety in a meal enhances food intake in man.

Physiol Behav. 1981 Feb; 26(2):215-21.

Roth AE. Repugnance as a Constraint on Markets. J Econ Perspect. 2007; 21(3):37-58.

Samuelson W, Zeckhauser RJ. Status quo bias in decision making. J Risk Uncertainty.

1988; 1:7-59.

48

Schnitzler MA, Whiting JF, Brennan DC, et al. The life-years saved by a deceased organ

donor. Am J Transplant. 2005 Sep; 5(9):2289-96.

Schnitzler MA, Lentine KL, Burroughs TE. The cost effectiveness of deceased organ

donation. Transplantation. 2005 Dec; 80(11):1636-7.

Schroeder SA. We can do better - Improving the health of the American people. N Engl J

Med. 2007 Sep; 357(12):1221-8.

Schultz W, Nolan J, Cialdini R, et al. The constructive, destructive, and reconstructive

power of social norms. Psychol Sci. 2007 May; 18(5):429-34.

Schweitzer M, Hershey JC, Asch DA. Individual choice in spending accounts: Can we

rely on employees to choose well? Med Care. 1996 Jun; 34(6):583-93.

Shang J, Croson R. A field experiment in charitable contribution: The impact of social

information on the voluntary provision of public goods. Econ J. 2009 Oct;

119:1422-39.

Simon HA. Models of Man: Social and Rational – Mathematical Essays on Rational

Human Behavior in a Social Setting. New York: Wiley; 1957.

Stockwell MS, Kharbanda EO, Martinez RA, et al. Effect of a text messaging

intervention on influenza vaccination in an urban, low-income pediatric and

adolescent population: A randomized controlled trial. JAMA. 2012 Apr 25;

307(16):1702-8.

Sunstein CR. Nudges.gov: Behavioral economics and regulation. In: Zamir E, Teichman

D, editors. Oxford Handbook of Behavioral Economics and the Law.

Forthcoming.

49

Thaler RH. (2009). Opting in versus opting out. New York Times [Internet]. 2009 Sep 26

[cited 2013 Mar 25]. Available from:

http://www.nytimes.com/2009/09/27/business/economy/27view.html

Thaler RH, Sunstein CR. Nudge: improving decisions about health, wealth, and

happiness. New Haven: Yale University Press; 2008.

Titmuss RM. Gift Relationship: From Human Blood to Social Policy. New York:

Pantheon; 1971.

Tversky A, Kahneman D. The framing of decisions and the psychology of choice.

Science. 1981 Jan 30; 211(4481):453-8.

United Network for Organ Sharing (US) [Internet]. Richmond: United Network for

Organ Sharing; Date unknown [cited 2013 Mar 25]. Available from:

http://www.unos.org/donation/index.php?topic=data_resources.

Variyam JN, Cawley J. Nutrition labels and obesity. 2006 Jan; NBER Working Paper,

No. 11956.

Variyam JN. Do nutrition labels improve dietary outcomes? Health Econ. 2008 Jun;

17(6):695-708.

Vervloet M, Linn AJ, van Weert JCM, et al. The effectiveness of interventions using

electronic reminders to improve adherence to chronic medication: A systematic

review of the literature. J Am Med Inform Assoc. 2012 Sep. 19(5):696-704.

Volpp KG, Asch DA, Galvin R, et al. Redesigning employee health incentives —

Lessons from behavioral economics. N Engl J Med. 2011 Aug 4; 365(5):388-90.

50

Volpp KG, John L, Troxel AB, et al. Financial incentive-based approaches for weight

loss: A randomized trial. JAMA. 2008 Dec 10; 300(22):2631-7.

Volpp KG, Loewenstein G, Troxel AB, et al. A test of financial incentives to improve

warfarin adherence. BMC Health Serv Res. 2008 Dec 23; 8:272.

Volpp KG, Troxel AB, Pauly MV, et al. A randomized, controlled trial of financial

incentives for smoking cessation. N Engl J Med. 2009 Feb 12; 360(7):699-709.

Wansink B, Cheney MM. Super bowls: Serving bowl size and food consumption. JAMA.

2005 Apr 13; 293(14):1727-8.

Wansink B, van Ittersum K. Portion size me: Downsizing our consumption norms. J Am

Diet Assoc. 2007 Jul; 107(7):1103-6.

Wisdom J, Downs JS, Lowenstein G. Promoting healthy choices: information versus

convenience. Am Econ J Appl Econ. 2010 Apr; 2(2):164-78.

Zikmund-Fisher B, Fagerlin A, Roberts T, et al. Alternate methods of framing

information about medication side effects: Incremental risk versus total risk

occurrence. J Health Commun. 2008 Mar; 13(2):107-24.